Protein ML Senior Principal Data Scientist

In this role, you will harness the power of protein language models and foundational AI to predict immunogenicity, stability, and manufacturability — properties that are critical to bringing safe, effective vaccines to the world. Working at the interface of computational and experimental science, you will collaborate closely with antigen design and bench lab teams to iteratively refine protein property predictors and translate computational insights into real-world vaccine candidates.

Main Responsibilities

• Develop state-of-the-art protein property prediction models with a focus on immunogenicity, stability, and manufacturability to support vaccine design.
• Leverage and refine language-based foundational protein representations to accelerate the identification and optimization of vaccine candidates.
• Analyze complex relationships between protein sequences, structures, and functional data to uncover generalizable representations applicable across vaccine programs.
• Collaborate with structural biology, protein design, and bench lab teams to shape the strategy for protein modeling and to iteratively improve model quality.
• Maintain the modeling codebase according to best coding practices, ensuring results are robust, reproducible, and well-documented.
• Mentor and upskill colleagues on the latest advances in protein modeling tools and techniques, fostering a culture of continuous learning and scientific rigor.
• Communicate scientific findings clearly and compellingly to a broad range of internal and external stakeholders, adapting the level of detail to each audience.

About You

Education: PhD degree in Computational Biology, Data Science, Applied Mathematics, Biophysics or a closely related field.

Experience: 2+ years of industry or academic setting experience.

Preferred Qualifications

• Exceptional track record in computational biology, encompassing protein language models, biophysics, and the development of AI-based predictive models; prior experience with immunogenicity predictors is a plus.
• Advanced proficiency in Python, R, and SQL, with hands-on experience in PyTorch and AI-based structural modeling tools such as AlphaFold, OpenFold, and Boltz.
• Deep knowledge of protein representation models such as ESM.
• Familiarity with molecular dynamics, next-generation sequencing analysis, and deep mutational scanning is an advantage.
• Familiarity with agentic tools such as Claude Code for automated software engineering strongly preferred.
• Demonstrated ability to communicate fluently — in writing and in person — across computational and bench lab teams, adapting messaging to diverse audiences including senior leadership.
• Proven ability to mentor junior colleagues and lead collaboratively in the area of protein modeling.
• Strong problem-solving, communication, and collaboration skills across multidisciplinary teams.

Language: Fluent English required.